Chapter 1 Section 2.ppt

Section 1.2-‹#›
Copyright © 2014, 2012, 2010 Pearson Education, Inc.
Lecture Slides
Elementary Statistics
Twelfth Edition
and the Triola Statistics Series
by Mario F. Triola

Section 1.2-‹#›
Chapter 1
Introduction to Statistics
1-1 Review and Preview
1-2 Statistical and Critical Thinking
1-3 Types of Data
1-4 Collecting Sample Data

Section 1.2-‹#›
Key Concept
This section provides an overview of the
process involved in conducting a
statistical study:
•Prepare
•Analyze
•Conclude

Section 1.2-‹#›
Prepare - Context
 What do the data mean?
 What is the goal of the study?

Section 1.2-‹#›
Prepare - Source of the Data
 Is the source objective?
 Is the source biased?
 Be vigilant and skeptical of studies from
sources that may be biased.

Section 1.2-‹#›
Prepare - Sampling Method
 Does the method chosen greatly
influence the validity of the conclusion?
 Voluntary response (or self-selected)
samples often have bias (those with
special interest are more likely to
participate).
 Other methods are more likely to
produce good results.

Section 1.2-‹#›

Section 1.2-‹#›
Analyze – Graph and
Explore
 Every analysis should begin with
appropriate graphs (Chapter 2).

Section 1.2-‹#›
Analyze – Apply Statistical
Methods
 Later chapters describe important
statistical methods.
 With technology, good analysis does not
require strong computational skills, but
it does require using common sense
and paying attention to sound statistical
methods.

Section 1.2-‹#›
Conclude – Statistical
Significance
 Statistical significance is achieved in a
study when we get a result that is very
unlikely to occur by chance.

Section 1.2-‹#›
Conclude - Practical
Significance
 State practical implications of the
results.
 Common sense might suggest that the
finding does not make enough of a
difference to justify its use or to be
practical.

Section 1.2-‹#›
Example
 In a test of the Atkins weight loss
program, 40 subjects had a mean
weight loss of 4.6 pounds after one
year.
 Using formal methods of statistical
analysis, we can conclude the diet
appears to be effective.

Section 1.2-‹#›
Example - continued
 However, although 4.6 pounds is
statistically significant, using common
sense, it does not seem very
worthwhile.

Section 1.2-‹#›
Potential Pitfalls – Misleading
Conclusions
 Concluding that one variable causes the
other variable when in fact the variables are
only correlated or associated together.
Two variables that may seemed linked, are
smoking and pulse rate.
We cannot conclude the one causes the
other. Correlation does not imply causality.

Section 1.2-‹#›
Potential Pitfalls - Small Samples
Conclusions should not be based on
samples that are far too small.
Example: Basing a school suspension rate
on a sample of only three students

Section 1.2-‹#›
Potential Pitfalls - Loaded
Questions
If survey questions are not worded
carefully, the results of a study can be
misleading.
97% yes: “Should the President have the
line item veto to eliminate waste?”
57% yes: “Should the President have the
line item veto, or not?”

Section 1.2-‹#›
Potential Pitfalls - Order of
Questions
Questions are unintentionally loaded by
such factors as the order of the items
being considered.
Would you say traffic contributes more or
less to air pollution than industry? Results:
traffic - 45%; industry - 27%
When order reversed.
Results: industry - 57%; traffic - 24%

Section 1.2-‹#›
Potential Pitfalls - Nonresponse
Occurs when someone either refuses to
respond to a survey question or is
unavailable.
People who refuse to talk to pollsters have
a view of the world around them that is
markedly different than those who will let
pollsters into their homes.

Section 1.2-‹#›
Potential Pitfalls - Missing Data
Can dramatically affect results.
Subjects may drop out for reasons
unrelated to the study.
Example - People with low incomes are
less likely to report their incomes.
Example – U.S. Census suffers from
missing people (tend to be homeless or
low income).

Section 1.2-‹#›
Potential Pitfalls - Precise
Numbers
Because as a figure is precise, many
people incorrectly assume that it is also
accurate.
A precise number can be an estimate, and
it should be referred to that way.

Section 1.2-‹#›
Potential Pitfalls - Percentages
Misleading or unclear percentages are
sometimes used.
Example – Continental Airlines ran an ad
claiming “We’ve already improved 100% in
the last six months” with respect to lost
baggage.
Does this mean Continental made no
mistakes?

Chapter 1 Section 2.ppt

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